{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:FGOVA3XF2JO26QZ2GQX3V3BGEN","short_pith_number":"pith:FGOVA3XF","canonical_record":{"source":{"id":"2212.04461","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:36:41Z","cross_cats_sorted":[],"title_canon_sha256":"a015056cb1b4f5f12cb4738df316421999ba04b35c7a2cd2dcd4fbb591a565e6","abstract_canon_sha256":"d301c114efc48d62de411c57ee7fb24da63b1cf22b175ee4520bba114cd1a3c4"},"schema_version":"1.0"},"canonical_sha256":"299d506ee5d25daf433a342fbaec26235b58687a7112ea1c913594723d255ad3","source":{"kind":"arxiv","id":"2212.04461","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.04461","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"arxiv_version","alias_value":"2212.04461v1","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.04461","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"pith_short_12","alias_value":"FGOVA3XF2JO2","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"pith_short_16","alias_value":"FGOVA3XF2JO26QZ2","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"pith_short_8","alias_value":"FGOVA3XF","created_at":"2026-07-05T05:23:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:FGOVA3XF2JO26QZ2GQX3V3BGEN","target":"record","payload":{"canonical_record":{"source":{"id":"2212.04461","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:36:41Z","cross_cats_sorted":[],"title_canon_sha256":"a015056cb1b4f5f12cb4738df316421999ba04b35c7a2cd2dcd4fbb591a565e6","abstract_canon_sha256":"d301c114efc48d62de411c57ee7fb24da63b1cf22b175ee4520bba114cd1a3c4"},"schema_version":"1.0"},"canonical_sha256":"299d506ee5d25daf433a342fbaec26235b58687a7112ea1c913594723d255ad3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:23:41.469720Z","signature_b64":"5gA4aSZEW8zRlL0SJDs0muEAxvgAuK8bKsuTx3x7snH/3vEQNrk17zZb9rnpNz4pzYFUXHE1vtXpMtNwRdLZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"299d506ee5d25daf433a342fbaec26235b58687a7112ea1c913594723d255ad3","last_reissued_at":"2026-07-05T05:23:41.469272Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:23:41.469272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.04461","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:23:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iPeyZSTXCvTiJWL0dXbHQSNeFiVynMAfo7l8v81rpbzsxZD4FBQGco0BTz+mbzidTw/lOTGNWThI47J2ai+jAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:42:43.682828Z"},"content_sha256":"7bf1004800b2b5ecc1d35905db8b2b915efdf76ecc363592d155b428f2a45cb2","schema_version":"1.0","event_id":"sha256:7bf1004800b2b5ecc1d35905db8b2b915efdf76ecc363592d155b428f2a45cb2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:FGOVA3XF2JO26QZ2GQX3V3BGEN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leveraging Unlabeled Data to Track Memorization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hanie Sedghi, Mahsa Forouzesh, Patrick Thiran","submitted_at":"2022-12-08T18:36:41Z","abstract_excerpt":"Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural networks. Susceptibility is simple and easy to compute during training. Moreover, it does not require access to ground-truth labels and it only uses unlabeled data. We empirically show the effectiveness of our metric in tracking memorization on various architectures and datasets and p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.04461","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.04461/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:23:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jbj3GSWgR5eperjkwIIaKoBoCwu4kFxDAa2jW5h3rcCqBjgpQ6sjyXU4Cs8r8hqcqVUoaawJ69RK7dVMdFR4Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:42:43.683202Z"},"content_sha256":"3e7e3750fa1321ba176698fd2c3b0eb4288fe3569afd547d58ffa26c5b2852ad","schema_version":"1.0","event_id":"sha256:3e7e3750fa1321ba176698fd2c3b0eb4288fe3569afd547d58ffa26c5b2852ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FGOVA3XF2JO26QZ2GQX3V3BGEN/bundle.json","state_url":"https://pith.science/pith/FGOVA3XF2JO26QZ2GQX3V3BGEN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FGOVA3XF2JO26QZ2GQX3V3BGEN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T20:42:43Z","links":{"resolver":"https://pith.science/pith/FGOVA3XF2JO26QZ2GQX3V3BGEN","bundle":"https://pith.science/pith/FGOVA3XF2JO26QZ2GQX3V3BGEN/bundle.json","state":"https://pith.science/pith/FGOVA3XF2JO26QZ2GQX3V3BGEN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FGOVA3XF2JO26QZ2GQX3V3BGEN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FGOVA3XF2JO26QZ2GQX3V3BGEN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d301c114efc48d62de411c57ee7fb24da63b1cf22b175ee4520bba114cd1a3c4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:36:41Z","title_canon_sha256":"a015056cb1b4f5f12cb4738df316421999ba04b35c7a2cd2dcd4fbb591a565e6"},"schema_version":"1.0","source":{"id":"2212.04461","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.04461","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"arxiv_version","alias_value":"2212.04461v1","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.04461","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"pith_short_12","alias_value":"FGOVA3XF2JO2","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"pith_short_16","alias_value":"FGOVA3XF2JO26QZ2","created_at":"2026-07-05T05:23:41Z"},{"alias_kind":"pith_short_8","alias_value":"FGOVA3XF","created_at":"2026-07-05T05:23:41Z"}],"graph_snapshots":[{"event_id":"sha256:3e7e3750fa1321ba176698fd2c3b0eb4288fe3569afd547d58ffa26c5b2852ad","target":"graph","created_at":"2026-07-05T05:23:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.04461/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural networks. Susceptibility is simple and easy to compute during training. Moreover, it does not require access to ground-truth labels and it only uses unlabeled data. We empirically show the effectiveness of our metric in tracking memorization on various architectures and datasets and p","authors_text":"Hanie Sedghi, Mahsa Forouzesh, Patrick Thiran","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:36:41Z","title":"Leveraging Unlabeled Data to Track Memorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.04461","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7bf1004800b2b5ecc1d35905db8b2b915efdf76ecc363592d155b428f2a45cb2","target":"record","created_at":"2026-07-05T05:23:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d301c114efc48d62de411c57ee7fb24da63b1cf22b175ee4520bba114cd1a3c4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:36:41Z","title_canon_sha256":"a015056cb1b4f5f12cb4738df316421999ba04b35c7a2cd2dcd4fbb591a565e6"},"schema_version":"1.0","source":{"id":"2212.04461","kind":"arxiv","version":1}},"canonical_sha256":"299d506ee5d25daf433a342fbaec26235b58687a7112ea1c913594723d255ad3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"299d506ee5d25daf433a342fbaec26235b58687a7112ea1c913594723d255ad3","first_computed_at":"2026-07-05T05:23:41.469272Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:23:41.469272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5gA4aSZEW8zRlL0SJDs0muEAxvgAuK8bKsuTx3x7snH/3vEQNrk17zZb9rnpNz4pzYFUXHE1vtXpMtNwRdLZCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:23:41.469720Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.04461","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7bf1004800b2b5ecc1d35905db8b2b915efdf76ecc363592d155b428f2a45cb2","sha256:3e7e3750fa1321ba176698fd2c3b0eb4288fe3569afd547d58ffa26c5b2852ad"],"state_sha256":"d5304746114caaa8ad3055c54dc8dab86ed40f875f990608ec86c0e0fe90c8ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wik6U0g7ZohmAkWYPUKKC6tkSMMpWf0OSUg+XDBJQqaFkjbTSsBh+X1OWnv3PwTBEe3gB1e0yzrlpQmsB0jXAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:42:43.685274Z","bundle_sha256":"ba49bf43d821a1c824d27a2eed8b4339242b4fa669717705e98c470088d5c233"}}